Abrahart, R.J. and See, L. (2002). Multi-model data fusion for river flow forecasting: An evaluation of six alternative methods based on two contrasting catchments, <em>Hydrology and Earth System Sciences </em><bold>6</bold>(4): 655-670.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.5194/hess-6-655-2002" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.5194/hess-6-655-2002</a></dgdoi:pub-id>
Chalimourda, A., Scho¨lkopf, B. and Smola, A.J. (2004). Experimentally optimal <em>ν </em>in support vector regression for different noise models and parameter settings, <em>Neural Networks: The Official Journal of the International Neural Network Society </em><bold>17</bold>(1): 127-41.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1016/S0893-6080(03)00209-0" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/S0893-6080(03)00209-0</a></dgdoi:pub-id>
Cherkassky, V. and Ma, Y. (2004). Practical selection of SVM parameters and noise estimation for SVM regression, <em>Neural Networks: The Official Journal of the International Neural Network Society </em><bold>17</bold>(1): 113-26.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1016/S0893-6080(03)00169-2" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/S0893-6080(03)00169-2</a></dgdoi:pub-id>
Coulibaly, P., Hache´, M., Fortin, V. and Bobe´e, B. (2005). Improving daily reservoir inflow forecasts with model combination, <em>Journal of Hydrologic Engineering </em><bold>10</bold>(2): 91.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1061/(ASCE)1084-0699(2005)10:2(91)" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1061/(ASCE)1084-0699(2005)10:2(91)</a></dgdoi:pub-id>
De Vos, N.J. and Rientjes, T.H.M. (2005). Constraints of artificial neural networks for rainfall-runoff modelling: Trade-offs in hydrological state representation and model evaluation, <em>Hydrology and Earth System Sciences </em><bold>9</bold>(1-2): 111-126.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.5194/hess-9-111-2005" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.5194/hess-9-111-2005</a></dgdoi:pub-id>
Deng, Y.-F., Jin, X. and Zhong, Y.-X. (2005). Ensemble SVR for prediction of time series, <em>Proceedings of the International Conference on Machine Learning and Cybernetics, Guangzhou, China</em>, Vol. 2, pp. 734-748.
Fraley, C. and Hesterberg, T. (2009). Least angle regression and LASSO for large datasets, <em>Statistical Analysis and Data Mining </em><bold>1</bold>(4): 251-259.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1002/sam.10021" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1002/sam.10021</a></dgdoi:pub-id>
Hastie, T., Tibshirani, R. and Friedman, J. (2009). <em>The Elements of Statistical Learning: Data Mining, Inference and Prediction</em>, 2nd Edn., Springer, New York, NY.
Hyndman, R.J., Slava R. and Schmidt, D. (2012). <em>forecast: Forecasting functions for time series and linear models</em>, R package version 3.19, http://CRAN.R-project.org/package=forecast
Kim, T., Heo, J.-H. and Jeong, C.-S. (2006). Multireservoir system optimization in the Han River basin using multi-objective genetic algorithms, <em>Hydrological Processes </em><bold>20</bold>(9): 2057-2075.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1002/hyp.6047" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1002/hyp.6047</a></dgdoi:pub-id>
Legates, D.R. and McCabe, G.J. (1999). Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model validation, <em>Water Resources Research </em><bold>35</bold>(1): 233-241.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1029/1998WR900018" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1029/1998WR900018</a></dgdoi:pub-id>
Nash, J. and Sutcliffe, J. (1970). River flow forecasting through conceptual models, I: A discussion of principles, <em>Journal of Hydrology </em><bold>10</bold>(3): 282-290.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1016/0022-1694(70)90255-6" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/0022-1694(70)90255-6</a></dgdoi:pub-id>
Pucheta, J., Patino, D. and Kuchen, B. (2009). A statistically dependent approach for the monthly rainfall forecast from one point observations, <em>in </em>D. Li and Z. Chunjiang (Eds.), <em>Computer and Computing Technologies in Agriculture II, Volume 2</em>, IFIP Advances in Information and Communication Technology, Vol. 294, Springer, Boston, MA, pp. 787-798.
Siwek, K., Osowski, S., Szupiluk, R. (2009). Ensemble neural network approach for accurate load forecasting in a power system, <em>International Journal of Applied Mathematics and Computer Science </em><bold>19</bold>(2): 303-315, DOI: <a href="https://doi.org/10.2478/v10006-009-0026-2." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.2478/v10006-009-0026-2.</a><dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.2478/v10006-009-0026-2" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.2478/v10006-009-0026-2</a></dgdoi:pub-id>
Schölkopf, B. and Smola, A.J. (2002). <em>Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond</em>, Adaptive Computation and Machine Learning, Vol. 98, MIT Press, Cambridge, MA.
Schoölkopf, B. and Smola, A.J. (2004). A tutorial on support vector regression, <em>Statistics and Computing </em><bold>14</bold>(3): 199-122.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1023/B:STCO.0000035301.49549.88" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1023/B:STCO.0000035301.49549.88</a></dgdoi:pub-id>
Shrestha, D.L. and Solomatine, D.P. (2006). Machine learning approaches for estimation of prediction interval for the model output, <em>Neural Networks </em><bold>19</bold>(2): 225-235.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1016/j.neunet.2006.01.012" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.neunet.2006.01.012</a></dgdoi:pub-id>
Solomatine, D.P. and Ostfeld, A. (2008). Data-driven modelling: Some past experiences and new approaches, <em>Journal of Hy-droinformatics </em><bold>10</bold>(1): 3.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.2166/hydro.2008.015" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.2166/hydro.2008.015</a></dgdoi:pub-id>
Syed, A.R. (2011). A review of cross validation and adaptive model selection, <em>Statistics</em>, Mathematics Theses, Georgia State University, Arlanta, GA, Paper 99.
Timmermann, A. (2006). Forecast combinations, <em>in </em>G. Elliott, C. Granger and A. Timmermann (Eds.), <em>Handbook of Economic Forecasting</em>, Elsevier, Amsterdam, Chapter 4, pp. 135-196.
Wichard, J. (2011). Forecasting the NN5 time series with hybrid models, <em>International Journal of Forecasting </em><bold>27</bold>(3): 700-707.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1016/j.ijforecast.2010.02.011" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.ijforecast.2010.02.011</a></dgdoi:pub-id>
Wichard, J. and Ogorzalek, M. (2007). Time series prediction with ensemble models applied to the CATS benchmark, <em>Neurocomputing </em><bold>70</bold>(13-15): 2371-2378.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1016/j.neucom.2005.12.136" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.neucom.2005.12.136</a></dgdoi:pub-id>
Wu, C., Chau, K. and Li, Y. (2008). River stage prediction based on a distributed support vector regression, <em>Journal of Hydrology </em><bold>358</bold>(1-2): 96-111.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1016/j.jhydrol.2008.05.028" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.jhydrol.2008.05.028</a></dgdoi:pub-id>
Xiong, L., Shamseldin, A. Y. and Oconnor, K. (2001). A non-linear combination of the forecasts of rainfall-runoff models by the first-order Takagi-Sugeno fuzzy system, <em>Journal of Hydrology </em><bold>245</bold>(1-4): 196-217.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1016/S0022-1694(01)00349-3" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/S0022-1694(01)00349-3</a></dgdoi:pub-id>
Yang, Y., Lin, H., Guo, Z. and Jiang, J. (2007). A data mining approach for heavy rainfall forecasting based on satellite image sequence analysis, <em>Computers Geosciences </em><bold>33</bold>(1): 20-30.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1016/j.cageo.2006.05.010" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.cageo.2006.05.010</a></dgdoi:pub-id>
Zaman, M. and Hirose, H. (2011). Classification performance of bagging and boosting type ensemble methods with small training sets, <em>New Generation Computing </em><bold>29</bold>(3): 277-292.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1007/s00354-011-0303-0" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1007/s00354-011-0303-0</a></dgdoi:pub-id>